mrmuminov commited on
Commit
3b5175f
1 Parent(s): 04e7483

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +6 -6
app.py CHANGED
@@ -8,7 +8,7 @@ from transformers.pipelines.audio_utils import ffmpeg_read
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  import tempfile
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  import os
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- MODEL_NAME = "dataprizma/whisper-medium-uz"
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  BATCH_SIZE = 8
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  FILE_LIMIT_MB = 1000
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  YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files
@@ -18,14 +18,14 @@ device = 0 if torch.cuda.is_available() else "cpu"
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  pipe = pipeline(
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  task="automatic-speech-recognition",
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  model=MODEL_NAME,
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- chunk_length_s=30,
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  device=device,
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  )
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  def transcribe(inputs, task):
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  if inputs is None:
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- raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
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  text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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  return text
@@ -99,7 +99,7 @@ mf_transcribe = gr.Interface(
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  outputs="text",
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  layout="horizontal",
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  theme="huggingface",
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- title="Whisper Medium Uzbek: Transcribe Audio",
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  description=(
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  "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
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  f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
@@ -117,7 +117,7 @@ file_transcribe = gr.Interface(
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  outputs="text",
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  layout="horizontal",
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  theme="huggingface",
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- title="Whisper Medium Uzbek: Transcribe Audio",
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  description=(
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  "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
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  f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
@@ -135,7 +135,7 @@ yt_transcribe = gr.Interface(
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  outputs=["html", "text"],
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  layout="horizontal",
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  theme="huggingface",
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- title="Whisper Medium Uzbek: Transcribe YouTube",
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  description=(
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  "Transcribe long-form YouTube videos with the click of a button! Demo uses the OpenAI Whisper checkpoint"
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  f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
 
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  import tempfile
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  import os
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+ MODEL_NAME = "dataprizma/whisper-large-v3-turbo"
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  BATCH_SIZE = 8
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  FILE_LIMIT_MB = 1000
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  YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files
 
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  pipe = pipeline(
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  task="automatic-speech-recognition",
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  model=MODEL_NAME,
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+ chunk_length_s=15,
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  device=device,
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  )
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  def transcribe(inputs, task):
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  if inputs is None:
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+ raise gr.Error("Fayl tanlanmadi yoki yuklashad xatolik! Iltimos qaytadan urinib ko'ring.")
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  text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
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  return text
 
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  outputs="text",
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  layout="horizontal",
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  theme="huggingface",
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+ title="Whisper Uzbek: Transcribe Audio",
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  description=(
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  "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
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  f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
 
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  outputs="text",
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  layout="horizontal",
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  theme="huggingface",
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+ title="Whisper Uzbek: Transcribe Audio",
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  description=(
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  "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
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  f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
 
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  outputs=["html", "text"],
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  layout="horizontal",
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  theme="huggingface",
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+ title="Whisper Uzbek: Transcribe YouTube",
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  description=(
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  "Transcribe long-form YouTube videos with the click of a button! Demo uses the OpenAI Whisper checkpoint"
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  f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"